The efficient processing and monitoring of large amounts of data for anomalies, associations, and clusters is becoming increasingly important as governments, businesses, entities and individuals store and/or require access to growing amounts of data.
This data is often stored in databases. Effectively monitoring data for anomalies, association and clustering has numerous applications. Examples of such applications include network intrusion detection, credit card fraud, calling card fraud, insurance claim and accounting inefficiencies or fraud, electronic auction fraud, cargo shipment faults, and many others. In addition to revealing suspicious, illegal or fraudulent behavior, anomaly detection is useful for spotting rare events, as well as for the vital task of data cleansing or filtering.
Traditional approaches to anomaly, association and clustering detection have focused on numerical databases, while approaches for categorical databases are few. Typically, numerical databases can be converted into categorical form, but categorical databases are often difficult and expensive to convert into numerical form.